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Title: Uncertainty Quantification on SAM Simulations of EBR-II Loss-of-Flow Tests

Abstract

The System Analysis Module (SAM) is a modern system analysis tool being developed at Argonne National Laboratory for advanced non-LWR safety analysis. It aims to provide fast-running, whole-plant transient analyses capability with improved-fidelity for SFR, LFR, and MSR/FHR. In prior work, SAM has performed well in modeling the EBR-II benchmark based on the SHRT-17 and SHRT-45R protected and unprotected loss-of-flow experiments. However, underlying uncertainties in the modeling parameters need to be quantified to gain confidence in any best-estimate simulations. In this work, a Python coupling framework between Dakota and SAM has been developed to investigate these uncertainties, alongside code enhancements in SAM for user-flexibility in perturbing physical closure models and fluid equations of state. 25 significant input parameters covering boundary conditions, geometry, physical closure models, and equations of state were identified with their uncertainties defined from literature. Two SAM transient models for SHRT-17 and SHRT-45R were perturbed on these input parameters using Latin Hypercube Sampling and the uncertainties in predicted maximum fuel, cladding, and coolant temperatures were quantified as the output responses. Spearman rank correlation of input parameters to the predicted temperatures indicated that the most sensitive parameters include the initial core power, pipe wall drag, fuel & coolant conductivity,more » and peak channel flow area. Sensitivity of input parameters was found to differ between the two transients and core channel modeling strategies. The overall perturbations did not result in large uncertainties in the predicted temperatures, despite the efforts made to obtain accurate yet conservative input uncertainties.« less

Authors:
; ;
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Nuclear Energy - Nuclear Energy Advanced Modeling and Simulation (NEAMS)
OSTI Identifier:
1596737
DOE Contract Number:  
AC02-06CH11357
Resource Type:
Conference
Resource Relation:
Conference: 18th International Topical Meeting on Nuclear Reactor Thermal Hydraulics, 08/18/19 - 08/23/19, Portland, OR, US
Country of Publication:
United States
Language:
English

Citation Formats

Mui, Travis, Hu, Rui, and Zhang, Guanheng. Uncertainty Quantification on SAM Simulations of EBR-II Loss-of-Flow Tests. United States: N. p., 2019. Web.
Mui, Travis, Hu, Rui, & Zhang, Guanheng. Uncertainty Quantification on SAM Simulations of EBR-II Loss-of-Flow Tests. United States.
Mui, Travis, Hu, Rui, and Zhang, Guanheng. Sun . "Uncertainty Quantification on SAM Simulations of EBR-II Loss-of-Flow Tests". United States.
@article{osti_1596737,
title = {Uncertainty Quantification on SAM Simulations of EBR-II Loss-of-Flow Tests},
author = {Mui, Travis and Hu, Rui and Zhang, Guanheng},
abstractNote = {The System Analysis Module (SAM) is a modern system analysis tool being developed at Argonne National Laboratory for advanced non-LWR safety analysis. It aims to provide fast-running, whole-plant transient analyses capability with improved-fidelity for SFR, LFR, and MSR/FHR. In prior work, SAM has performed well in modeling the EBR-II benchmark based on the SHRT-17 and SHRT-45R protected and unprotected loss-of-flow experiments. However, underlying uncertainties in the modeling parameters need to be quantified to gain confidence in any best-estimate simulations. In this work, a Python coupling framework between Dakota and SAM has been developed to investigate these uncertainties, alongside code enhancements in SAM for user-flexibility in perturbing physical closure models and fluid equations of state. 25 significant input parameters covering boundary conditions, geometry, physical closure models, and equations of state were identified with their uncertainties defined from literature. Two SAM transient models for SHRT-17 and SHRT-45R were perturbed on these input parameters using Latin Hypercube Sampling and the uncertainties in predicted maximum fuel, cladding, and coolant temperatures were quantified as the output responses. Spearman rank correlation of input parameters to the predicted temperatures indicated that the most sensitive parameters include the initial core power, pipe wall drag, fuel & coolant conductivity, and peak channel flow area. Sensitivity of input parameters was found to differ between the two transients and core channel modeling strategies. The overall perturbations did not result in large uncertainties in the predicted temperatures, despite the efforts made to obtain accurate yet conservative input uncertainties.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {8}
}

Conference:
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